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Copy file name to clipboardExpand all lines: docs/blog/_posts/2020-02-03-22nd-dotty-milestone-release.md
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---
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layout: blog-page
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title: Announcing Dotty 0.22.0-RC1 - syntactic enhancements, type-level arithmetics and more
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title: Announcing Dotty 0.22.0-RC1 - syntactic enhancements, type-level arithmetic and more
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author: Anatolii Kmetiuk
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authorImg: /images/anatolii.png
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date: 2020-02-03
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---
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Hello! We are excited to announce 0.22.0-RC1 of Dotty. This version brings syntactic enhancements for extension methods and contextual parameters, as well as the kind projector syntax. Other notable changes include type-level arithmetics, changes to the `inline` parameters semantics and suggestions on missing contextual parameters.
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Hello! We are excited to announce 0.22.0-RC1 of Dotty. This version brings syntactic enhancements for extension methods and contextual parameters, as well as the kind projector syntax. Other notable changes include type-level arithmetic, changes to the `inline` parameters semantics and suggestions on missing contextual parameters.
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You can try out this version right now, from the comfort of your SBT, by visiting the [home page](https://dotty.epfl.ch/) and scrolling down to the "Create a Dotty Project" section. Enjoy the ride🚀!
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This change was introduced by [PR #8060](https://github.com/lampepfl/dotty/pull/8060/). For more information about the inline capability of Dotty, see [documentation](https://dotty.epfl.ch/docs/reference/metaprogramming/inline.html).
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# Primitive compiletime operations on singleton types
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Contributed by [Maxime Kajaer](https://github.com/MaximeKjaer), this release brings along type-level arithmetics:
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Contributed by [Maxime Kjaer](https://github.com/MaximeKjaer), this release brings along type-level arithmetic:
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```scala
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importscala.compiletime.ops.int._
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|Required: (13:Int)
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```
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This feature is particularly useful for data science applications. In data science, it is very easy to make a linear algebra mistake, multiply matrices of wrong dimensions and get a runtime error – sometimes after a few hours of running the model. Hence compile-time verification of the models has a great potential for saving time. With such a type-level arithmetics, Scala becomes well-positioned to implement such type-safe data science frameworks.
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This feature is particularly useful for data science applications. In data science, it is very easy to make a linear algebra mistake, multiply matrices of wrong dimensions and get a runtime error – sometimes after a few hours of running the model. Hence compile-time verification of the models has a great potential for saving time. With such a type-level arithmetic, Scala becomes well-positioned to implement such type-safe data science frameworks.
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For the discussion, see [PR #7628](https://github.com/lampepfl/dotty/pull/7628). The documentation is available [here](https://dotty.epfl.ch/docs/reference/metaprogramming/inline.html#the-scalacompiletimeops-package).
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